Machine learning is a powerful area of ____ intelligence that helps computers learn from ____. Today, we will discuss five important concepts: linear regression, logistic regression, decision trees, random forests, and statistical analysis. Linear Regression is a method used to ____ a continuous number. For example, we can use linear regression to predict the price of a house ____ on its size. It finds the best straight line that fits the data points. Logistic Regression is different because it is used for ____ problems. It helps us ____ questions like “Yes” or “No.” For instance, we might want to know if a patient has a disease based on test results. Logistic regression changes the ____ into a ____ between 0 and 1. Decision Trees are simple models that make decisions by asking a series of questions. Each question ____ the data into smaller groups until the model gives a final decision. For example, a decision tree can help decide if you should play outside by asking questions about the weather. Random Forests use many decision trees ____. This method makes predictions more ____ by averaging the results of several trees. Each tree may give a different answer, but when ____, they create a stronger prediction. Finally, Statistical Analysis is important for understanding our data. It helps us find ____ and trends that can guide our decisions. We can use statistics to calculate the ____, variance, and correlation ____ variables.

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